Data science, without a doubt, deserves the tag of “the sexiest job of the 21st century”. Every year, the number of people joining the data science tribe is massive. However, if you look at the stage when people start to learn about data science is usually during their college days or after graduating, people are even taking up data science courses after working for years in a different domain. You can definitely make out the intriguing news.
But if you look at high schools in India, there are not many schools that have taken this much-needed initiative of making high schoolers familiar with this domain. And there are plenty of reasons behind this — and one of the main reasons is the education system in India where we are only focusing on passing exams. It is no surprise that many people have the notion that it’s not time for students to focus on data science — they should concentrate more on the “generic syllabus”.
However, it is high time that people get rid of this notion and include data science in the high school syllabus. In this article, we are going to look at the advantages of learning data science in high school itself and what approach teachers can take to go about the teaching process.
Why High Schools Should Include Data Science In Their Curriculum
You can’t deny the fact that kids of this generation are much smarter. And this is definitely because of the fact that technology has invaded a significant part of our lives. Also, if you look at the fact that kids today have a great interest in solving some of the most complex problems — even if you give them a business problem, they somehow manage to give you a solution (if not 100% feasible, then at least good enough to work on it).
And if data science is introduced to them in the high school level itself, the chance of them developing better skills increases significantly. Talking about the curriculum in high schools, most of the schools already have programming languages such as C, C++, Java etc. But data science has still not managed to take an important place, and that is what the education system needs to be fixed.
Simply put, even if a student is not focusing on landing a job in the domain, there are chances that they might have to have interaction with data science professionals. And having a strong understanding of data science makes interaction easier and full of insights.
How To Teach High Schoolers
Have A Clear Roadmap
When it comes to teaching data science to high school students, the first thing is to make them familiar with the term. Because, there are chances that they might have heard about it, but don’t exactly know in in-depth. It is imperative to have a roadmap when you are starting a new course at school. The goals and objectives of the course should also be made clear to the students.
Furthermore, give the students a glimpse of the opportunities data science posses — not only in terms of jobs in that specific domain but in terms of career as a whole. And when you are doing so, do not claim about the opportunities; rather, show them or tell them about real-time examples.
Do Not Onboard A Data Scientist, Onboard A Data Science Faculty
No doubt, data scientists are knowledgeable and are really good at what they do. But when it comes to teaching, you cannot rely on a data scientist, you need to have someone who has experience in teaching data science. The major reason is that the job of a data scientist varies industry to industry or company to company, one cannot expect a data scientist to know in-depth about all the other elements of data science. While on the other hand, a data science faculty would know about almost all the elements and sub-domains of data science.
Furthermore, teaching is completely different from working in the domain. Therefore, make sure that you have a data science faculty with prior experience in teaching.
Keep The Sessions Non-Technical
One of the major things to keep in mind when teaching data science to high school students is that you cannot everyone to be a tech-savvy. Every student has a different level of interests, skills, and expertise there you have to make sure that sessions are not much technical at the initial stages to retain the interest of the students. Eventually, when the technical aspect becomes necessary, then you can go about teaching them the technical stuff.
Keep Things Interactive
It might be really intriguing to go right into some of the core concepts, but that wouldn’t be a great thing to do when you are teaching students who are in high school. Therefore, make sure you start with the basics of the subject. Also, keep the sessions interactive by asking questions. It would not only help you understand whether the students are clear with the concepts and but also keep them engaged with the subject.
Also, it is advised to use interactive templates. It might be easy for a data science professional to understand plain numbers and b/w graphs, but students might get confused. Therefore, the data science faculty needs to be a great presenter as well who knows when, where and how to use interactive templates.
Do Not Rush
You cannot expect a high school student who is new to the data science domain, to understand all the concepts at a fast rate. Even experienced data science professionals sometimes take time to understand a concept. Therefore, keep things slow, make sure each and every student is clear with all the concepts. And for better understanding, take routine tests.
Collaborate With Leading Companies And Universities
It would be one of the greatest things if you collaborate with companies data are already in doing good in the data science space. This would help you to come with real-time examples and use cases for different concepts. As it is being said, “An example is always better than a claim”. Furthermore, you can also invite data science professionals from different sub-domains to have interaction with students. It would help the students in understanding the domain better — not only in terms of its use cases but also in terms of career.
Moreover, a collaboration with some university can also be done. This kinds of collaborations are really useful when a school struggles to onboard a faculty. Professors from universities can be invited to teach or guide the high school students.
Include Newer Programming Languages
Programming languages like C, C++, Java are already there in the curriculums of most of the schools. However, languages like Python, R, Scala are also imperative in data science. So, make sure that the school has all the tools and platforms required to code with these languages. The better the infrastructure, the more students engage.
Also, do not make it a generic programming session. Make sure the programming exercises and projects are interesting and are related to real use cases.
It is also advised to conduct routine programming test. It would help the students to learn the languages better and also help the instructor to understand which student is good at what and which student is lagging behind in which concept.
Register for our upcoming events:
- Meetup: NVIDIA RAPIDS GPU-Accelerated Data Analytics & Machine Learning Workshop, 18th Oct, Bangalore
- Join the Grand Finale of Intel Python HackFury2: 21st Oct, Bangalore
- Machine Learning Developers Summit 2020: 22-23rd Jan, Bangalore | 30-31st Jan, Hyderabad
Enjoyed this story? Join our Telegram group. And be part of an engaging community.
What's Your Reaction?
Harshajit is a writer / blogger / vlogger. A passionate music lover whose talents range from dance to video making to cooking. Football runs in his blood. Like literally! He is also a self-proclaimed technician and likes repairing and fixing stuff. When he is not writing or making videos, you can find him reading books/blogs or watching videos that motivate him or teaches him new things.